Run backtests on scalable cloud servers without requiring local hardware.
Access extensive historical and real-time data for equities, forex, crypto, futures, and more.
Built on the open-source LEAN algorithmic trading framework for reliable and scalable execution.
Seamlessly connect to brokers like Interactive Brokers for automated live trading.
Utilize Jupyter Notebooks for interactive data analysis and strategy development.
Tools for risk management, performance analytics, and portfolio allocation.
Share, discuss, and monetize algorithms through forums and a marketplace.
Develop and deploy automated trading systems for personal investment across various markets.
Validate financial models and hypotheses using historical data and simulation tools.
Implement scalable trading algorithms for institutional investments with risk management.
Use as a practical tool for courses in algorithmic trading and quantitative analysis.
Leverage market data and ML libraries to predict trends and optimize strategies.
Integrate trading capabilities into fintech products using APIs and cloud infrastructure.
Use portfolio tools to diversify assets and maximize returns based on quantitative insights.
Identify and exploit price discrepancies in real-time across different exchanges and assets.
Develop tailored investment strategies for clients using automated backtesting and analysis.
Connect trading algorithms to other software or databases via API for enhanced functionality.
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